tidyrun 0.0.5

TidyRun

TidyRun

A tool to orchestrate the compute and storage of Python DAGs

Features

Compute Orchestration

TidyRun provides first-class deferred compute primitives for DAG workflows:

• Deferred Primitives: Model work with Job, ParametrizedJob, and nested DAG
• Dependency-Aware Scheduling: Evaluate DAGs with topological execution and fail-fast behavior
• Execution Modes: Choose subprocess (default), thread, or process
• Parallel Evaluation: Run independent nodes with DAG.evaluate(max_workers=...)
• Materialized Plans: Compile reproducible execution plans before running jobs
• Resumable Runs: Re-run materialized plans with execute_materialized(skip_completed=True)
• Pluggable Executors: Use local executors, SlurmExecutor, or AwsBatchExecutor

Serialization and Storage

TidyRun also includes a comprehensive serialization system for storing and retrieving Python objects:

• Type-Aware Encoding: Automatically selects folder, Parquet, HDF5, JSON, or pickle based on value type
• Lazy Evaluation: Directories deserialize into LazyDict objects that load values on-demand
• Recursive Concatenation: Aggregate DataFrames across nested structures with LazyDict.concat() (optionally parallel with max_workers)
• Metadata Sidecars: Each output is tracked with .tidyrun metadata files for format versioning and checksums
• Checksum Return Value: serialize(...) returns checksum information (algorithm, digest) for the serialized payload
• Extensible Pipeline: Customize encoders or add support for custom types
• Intelligent Fallback: Parquet → HDF5 → JSON → Pickle chain ensures robust serialization

Quick Example:

Compute (DAG execution):

from tidyrun import DAG, Job


def square(x: int) -> int:
    return x * x


dag = DAG()
dag["a"] = Job(func=square, kwargs={"x": 3})

# Fast local execution without subprocess spawn overhead
outputs = dag.evaluate("./local_dag", execution_mode="thread", max_workers=4)
print(outputs["a"])  # 9

Serialization and lazy loading:

from tidyrun import serialize, deserialize
import pandas as pd

# Save nested data with smart format selection
serialize({
    "metrics": pd.DataFrame({"score": [9]}),
    "config": {"lr": 0.001},
}, "./results/exp_001")

# Load with lazy evaluation
results = deserialize("./results/exp_001")
df = results["metrics"]  # Loads on access

# Aggregate across nested structures
combined = results.concat(names=["run_id"])

Learn More:

• Quick Start — Local docs workflow and publishing notes
• DAG Guide — Jobs, parametrized jobs, executors, and evaluation modes
• Serialization Guide — Complete API reference, quick reference, and examples